Abstract
In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development of groundwater resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baldi P, Hornik K (1989) Neural networks and principal component analysis: learning from examples without local minima. Neural Netw 2(1):53–58
Caulibaly P, Anctil F, Bobee B (2001) Multivariate reservoir inflow forecasting using temporal neural networks. J Hydrol Eng (9–10):367–376
Coppola EJ, Anthony JR, Poulton M, Szidarovszky VW (2005) A neural network model for predicting aquifer level elevations. Groundwater 43(2):231–241
Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257
Daliakopoulos IN, Coulibaly P, Tsanis KI (2005) Groundwater level forecasting using artificial neural network. J Hydrol 229:229–240
French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using a neural network. J Hydrol 137:1–31
Govindaraju RS, Ramachandra RA (2000) Artificial neural networks in hydrology. Kluwer, Netherlands
Lallahem S, Mania J, Hani A, Najjar Y (2005) On the use of neural networks to evaluate groundwater level in fractured media. J Hydrol 307:92–111
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168
Maier HR, Dandy GC (1998) Understanding the behavior and optimizing the performance of back-propagation neural networks: an empirical study. Env Model Softw 13:179–191
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441
Nayak PC, Satyaji UR, Sudheer KP (2006) Groundwater level forecasting in shallow aquifer using artificial neural network approach. Water Resour Manage 20:77–90
Philip EG, Walter M (1978) Algorithms for the solution of the nonlinear least-squares problem. SIAM J Numer Anal 15(5):977–1137
Prasad RK, Mondal NC, Banerjee P, Nandakumar MV, Singh VS (2008) Deciphering potential groundwater zone in hard rock through the application of GIS. Env Geol 55(3):467–475
Prochazka A (1997) Seasonal time neural networks and -series prediction by artificial neural networks. Fifth International Conference, Digital Object Identifier, vol 440(7–9), pp 36–41
Sophocleous M (2005) Groundwater recharge and sustainability in the high plains aquifer in Kansas, USA. Hydrogeol J 13:351–365
Stinchcombe M, White M (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359–366
Tikhonov AN (1963) Solution of incorrectly formulated problems and the regularization method. Soviet Math Dokl 4:1035–1038 (English translation of Dokl Akad Nauk SSSR 151:501–504)
Voudouris KS (2006) Groundwater balance and safe yield of the coastal aquifer system in N. Eastern Korinthia. Greece. Appl Geogr 26 (3–4):291–31
Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62
Acknowledgments
The field study has been financed by INCID, Ministry of Water Resources, Government of India. The authors are grateful to Dr. V. P. Dimri, Director, National Geophysical Research Institute, Hyderabad, India for his kind permission to publish this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Banerjee, P., Prasad, R.K. & Singh, V.S. Forecasting of groundwater level in hard rock region using artificial neural network. Environ Geol 58, 1239–1246 (2009). https://doi.org/10.1007/s00254-008-1619-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00254-008-1619-z